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School of Computing

The School of Computing is currently inviting applications one four-year PhD scholarship. The scholarship, co-funded by EPSRC and the University, is for UK residents only. By UK residents, we mean people who "haveno restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship." For a more detailed explanation from EPSRC, please go to this web page.

The Project

The aim of this project would be to develop a framework using advanced Artificial Intelligence (AI) techniques based on a combination of deep and adversarial/reinforcement learning to derive the relationship in various areas of the brain using EEG when body is various position and orientation. The problem is ill defined as three domains (time, spatial and spectral) are non-linearly connected. This does not have straightforward algorithmic solution and requires sophisticated AI methods.

The applicant should ideally have prior experience in EEG signal processing and computational analysis using Matlab.

Start Date and Funding

The expected start date is 1 October 2018.

The scholarship will cover a bursary (£14,777 per year, 2018/19 rate) and the fees for home (UK/EU) students (£4,260, 2018/19 rate). The scholarships will be awarded annually for four years (42 months) for a full-time PhD student, subject to satisfactory progression through each year of study. They are awarded on a competitive basis and interview performance will be taken into account.

Support for research students includes:

regular supervision meetings

a research training programme

computer equipment

a desk in an office

funds for conference travel

How to Apply

Deadline: 31 May 2018

Applications should be made through the: University of Kent online admissions form with the PhD Project text, given above, pasted into the "Reasons for study" section.

Contact

The project will be supervised by Dr Palaniappan Ramaswamy. Dr Ramaswamy heads the Data Science Research Group with over two decades of research experience in utilising electroencephalogram (EEG) signals for various electrophysiological applications.